prvni draft LSTM

testy/ml/test.py

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+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from keras.models import Sequential
+from keras.layers import LSTM, Dense
+from v2realbot.controller.services import get_archived_runner_details_byID
+from v2realbot.common.model import RunArchiveDetail
+import json
+
+runner_id = "838e918e-9be0-4251-a968-c13c83f3f173"
+result = None
+res, set = get_archived_runner_details_byID(runner_id)
+if res == 0:
+    print("ok")
+else:
+    print("error",res,set)
+
+bars = set["bars"]
+indicators = set["indicators"]
+#print("bars",bars)
+#print("indicators",indicators)
+
+def scale_and_transform_data(bars, indicators):
+  """Scales and transforms the `bars` and `indicators` dictionaries to use in an RNN time series prediction model.
+
+  Args:
+    bars: A dictionary containing OHLCV values and a timestamp.
+    indicators: A dictionary containing additional indicators and a timestamp.
+
+  Returns:
+    A tuple containing the scaled and transformed training data, validation data, and test data.
+  """
+
+  # Combine the two dictionaries
+  #combined_data = {**bars, **indicators}
+  bar_data = np.column_stack((bars["time"], bars['high'], bars['low'], bars['volume'], bars['close'], bars['open']))
+
+  # Scale the data
+  scaler = StandardScaler()
+  scaled_data = scaler.fit_transform(bar_data)
+
+  # Create sequences of data
+  sequences = []
+  for i in range(len(scaled_data) - 100):
+    sequence = scaled_data[i:i + 100]
+    sequences.append(sequence)
+
+  # Split the data into training, validation, and test sets
+  train_sequences = sequences[:int(len(sequences) * 0.8)]
+  val_sequences = sequences[int(len(sequences) * 0.8):int(len(sequences) * 0.9)]
+  test_sequences = sequences[int(len(sequences) * 0.9):]
+
+  return train_sequences, val_sequences, test_sequences
+
+#Scale and transform the data
+train_sequences, val_sequences, test_sequences = scale_and_transform_data(bars, indicators)
+# Convert the training sequences to a NumPy array
+
+# Convert the training sequences array to a NumPy array
+train_sequences_array = np.asarray(train_sequences)
+
+# Reshape the training sequences to the correct format
+train_sequences_array = np.reshape(train_sequences_array, (train_sequences_array.shape[0], train_sequences_array.shape[1], 1))
+
+# Define the RNN model
+model = Sequential()
+model.add(LSTM(128, input_shape=(train_sequences_array.shape[1], train_sequences_array.shape[2])))
+model.add(Dense(1))
+
+# Compile the model
+model.compile(loss='mse', optimizer='adam')
+
+# Train the model on the sequence data
+model.fit(train_sequences, train_sequences, epochs=100)
+
+# Make a prediction for the next data point
+prediction = model.predict(test_sequences[-1:])
+
+# Print the prediction
+print(prediction)